As the dust of 2020 settles, industry analysts, manufacturers, and restaurateurs prepare to turn their attention to what the future holds. What are the trends that will dictate performance in 2023 and beyond?
At the top of the list is the proper gathering, analysis, and interpolation of data. Those players able to take advantage of the full range of extant data, including sanitation, energy, and ingredients, will be able to make more informed decisions, such as which menu items to offer, which ingredients are favored by diners, and how to reach the maximum demographic.
By the same token, those without the proper knowledge and infrastructure to store and access data could see their margins suffer, and their ability to outperform in the long run hampered. In this article, we’ll seek to impart the best practices and tools needed for gathering, managing, and deploying data in 2021–2023 for future success in the food sector.
Harvesting Data Trends: What to Look For and Where to Start
What’s the biggest data source of 2021? Depending on who you ask, the answer could range from consumer sentiment data to socio-economic statistics. Regardless, the more encompassing answer is: it all depends on your goals.
For restaurants, data can and should include sentiment, regional preferences, ingredient trends, and even sanitation practices. In terms of regional trends, food service providers should be seeking to identify which regional demographics are favoring certain products and keep an eye on adjacent regions to get a better understanding of their target demographic or any potential cuisines they could bring to their restaurants.
Ingredient-wise, data centers on populating specific restaurants with ingredients that best please their customers and gifting patrons with greater satisfaction and a memorable experience. Recipes must be informed, just like headquarters documents, by open-sourced data from public reviews and industry trend reports, and with artifically intelligent programs, harnessed to grand-scheme visions of corporate growth strategies.
In terms of sanitation practices, food service providers should explore data-based best practices, such as those outlined by OSHA, roundtable discussions, and industry reports. Data can also be used to track the products most susceptible to spoilage and adjust preparation and expiration guidelines accordingly.
Data Management and Gathering Solutions
The proliferation of data means providers must now learn how to properly manage and use it. Brizo’s food service data intelligence platform is one of many solutions designed to help research and gather insights, along with solutions such as menu management systems, which allow for the tracking of seasonal ingredients and local favorites.
Sales forecasting and lead tracking enable digital grocery and large-scale suppliers to optimize their inventory and shipment. Kelchner’s Food Forecasting simulator, for example, helps to keep track of customer data for the restaurant sector and compare their desired metrics with competitors’, in a bid to acquire additional customers.
On the coding front, Python and Tensorflow help further analyze customer insights. Python is a coding language used is to store, index, graph and manipulate data, while TensorFlow is a system used to develop AI-infused models based on past and present data.
When dealing with a broad data set, it’s often necessary to employ a data visualization tool such as Tableau or R Studio. Tools like these enable users to visualize their data quickly, accurately and share the information quickly.
Data Innovation and Prospecting
In the current climate, data innovation and prospecting are essential for success. Implementing practices and refining skills for more seamless data management can save organizations both time and resources, therefore increasing their efficiency.
We’ve seen a significant increase in restaurants utilizing advanced consumer data to build a single view of their customers while personalizing the customer experience to build measurable loyalty.
In terms of data analysis, AI and Machine Learning algorithms designed to interpret data are gaining traction. From customer-facing chatbots to personalized insight generation engines, many organizations, including C-suites, are employing Machine Learning to better understand their supply chain and customer demands.
Data-driven decision making has been critical since before the COVID-19 pandemic, and it’ll only become more important as restaurants move from recovery into the “new normal”.
